AI and Education: the Reality and the Potential

This is the script from a talk I gave at the Museum of London


You can watch a vide of the talk here:

While I was enjoying my morning latte on the tube earlier this month, I spotted this headline in the New Scientist: AI achieves its best ever mark on a set of English exam questions: i.e. the knowledge-based curriculum and exams. This is significant in three important ways, and these are also the three ways that I want to discuss AI and education with you this evening.

Firstly, it demonstrates the power of the AI that we can build to learn and to teach what we currently value in our education systems. This speaks to my first point that will be about the way AI can support teaching and learning.


Secondly, if this is headline news, then it demonstrates that we do not know enough about AI, because passing an exam is a very AI type problem to solve, and we should not be surprised that AI can do this. It should be something we take for granted, because we all understand enough about AI to know the basics of what it can and cannot achieve.


Thirdly, this headline draws our attention to the fact that we can build AI that can achieve what we set our students to achieve. The AI will get better and faster at this and it therefore is not intelligent to continue to educate humans to do what we can automate. We need to change our education systems to value our rich human intelligence. This need to change what and how we teach is also connected with the way that AI powers the automation that is changing our lives at some pace. We need very different skills, abilities and intelligences to thrive in the modern world. One only has to look at our current political failure in the UK, to see that the much-heralded education that we have provided for the last century has not provided our politicians with the emotional and social intelligence and the ability to solve problems collaboratively that the modern world requires. The need to change the what and how of teaching will be my third area for discussion tonight.


AI is empowering automation and the Fourth Industrial Revolution and its impact on education will be transformative, but what is this thing called AI?


A basic definition of AI is one that describes it as ‘technology that is capable of actions and behaviours that require intelligence when done by humans’. We may think of it as being the stuff of science-fiction, but actually it’s here and with us now from the voice-activated digital assistants that we use on our phones and in our homes, to the automatic passport gates that speed our transit through airports and the navigation apps that help us find our way around new cities and cities that we know quite well. We use AI every day, probably without giving it a thought.


The desire to create machines in our own image is not new, we have, for example, been keen on creating mechanical ‘human’ automata for centuries. However, the concept of AI was really born 63 years ago in September 1956 when 10 scientists at Dartmouth College in New Hampshire spent the summer working to create AI. If we look at the premise for this two-month study, we see that it is a premise that believes that: ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.’ And, although it seems incredibly arrogant now, the belief was that over a two-month period the team of scientists, would be able to make ‘a significant advance … in one or more of these problems.’


Following on from this there were some early successes. For example, expert systems that were used for tasks such as diagnosis in medicine. These systems were built from a series of rules through which the symptoms a patient presented with could be matched to potential diseases or causes, so enabling the doctor to make a decision about treatment. These systems were relatively successful, but they were limited because they could not learn. All of the knowledge that these expert systems could use to make decisions had to be written at the time the computer program was created. If new information was discovered about a particular disease or its symptoms, then if this was to be encompassed by the expert system it’s rule-based had to be changed. In the 1980s and 90s we had built useful systems, but certainly we were not anywhere near the dreams of the 1963 Dartmouth College conference. We plunged into what has been described as an AI winter where little significant progress was made and disappointment was felt by those who had such high expectations of what could be achieved.


Then in March 2016 came a game changing breakthrough. A breakthrough that was based on many years of research. A breakthrough that was made when Google Deepmind produced the AI system called AlphaGo that beat Lee Sedol, the world ‘Go’ champion. This was an amazing feat. A feat that could seem like magic, and whilst many of the techniques behind these machine learning algorithms are very sophisticated, these systems are not magic and they have their limitations. Smart as AlphaGo is, the real breakthrough was due to a combination that one might describe as a perfect storm. This perfect storm arises due to the combination of our ability to capture huge amounts of data, combined with the development of very sophisticated AI machine learning algorithms, plus affordable competing power and memory. These three factors when combined provide us with the ability to produce a system that could beat the world Go champion. Each of the elements in that perfect storm: the data, the sophisticated AI algorithms and the computing power and memory are important, but it is the data that has captured the imagination. And that has led to claims that ‘data is the new oil’, because it is the power behind AI and AI is a very profitable business, just like oil.


However, it’s important to remember, that just like oil, data is crude and it must be refined in order to derive its value. It must be refined by these AI algorithms. But even before the data can be processed by these algorithms it must be cleaned. So just like oil, there is a lot of work that needs to be done on the data before its value can be reaped. And even when we do reap this value from the data, it’s important to remember that machine learning is still basically just a form of pattern matching. Machine learning is certainly smart, very smart indeed, but it cannot learn everything.


AI has its limitations. For example, AI does not understand itself and struggles to explain the decisions that it makes. It has no common sense. If I ask you the audience this evening these questions: are you an empathetic friend? How well do you understand quantum physics? How are you feeling right now? Can you meditate? You will not struggle to answer, but AI would. So, the first important point to remember is that humans are intelligent in many ways. AI and Human Intelligence (HI) are not the same and the differences are extremely important, it is true that we have built our AI systems to be intelligent in the way that we perceive value in our human intelligence.


I remember in the early days of studying AI, the first grandmaster level Chess-playing Computer had been built and had beaten world champion Garry Kasparov. This seemed an amazing feat and there were people who thought that having cracked chess, which could be described as the pinnacle of human intelligence, intelligent people play chess after all. It was thought that we had cracked intelligence. And then people realised that the abilities that we take for granted, such as the ability to see, are far harder to achieve than is playing chess. Decades later, we have managed to build AI systems that can see, to an extent, but they still have their limitations.


What we need if we are to progress and grow our human intelligence, is to make sure that we recognise the need for humans to complementAI, not to mimic and repeat what the AI can do faster and more consistently than we can.


And so, what are the implications: the potential and the reality of AI within education? I believe that it is useful to think about this question from three perspectives:


1: using AI in education to tackle some of the big educational challenges;

2: educating people about AI so that they can use it safely and effectively;

3: changing education so that we focus on human intelligence and prepare people for an AI augmented world.


It is important to recognise that these three elements are not mutually exclusive. In fact, they are far from being mutually exclusive. They are interrelated in important ways.


Let us start with using AI in education to tackle some of the big educational challenges. Challenges such as the achievement gaps we see between those who achieve well educationally and those who do not. And challenges, such as those posed by learners with special and particular learning needs. If we start by looking at the reality of the systems that are available here and now, to help us tackle some of these challenges, then we will see the beginnings of the potential for the future. To start the ball rolling, I am going to handover to my friend Lewis Johnson, who runs an AI company in the US called Alelo. He can explain to you far better than I can exactly what is happening when it comes to data, AI and computing power in education.


Play video clip


Well, you heard it there from Lewis: Data has been a game changer when it comes to educational AI. And that’s true for companies working here in the UK too. If we take the London-based Century Tech, they have developed a machine learning platform that can personalise learning to the needs of individual students across curriculum areas to help them achieve their best. Their machine learning is informed by what we understand from neuroscience about the way the human brain learns. A further reality is that, in addition to being able to build intelligent platforms, such a Century, we can build intelligent tutors that can provide individual instruction to students in a specific subject area. These systems are extremely successful, not as successful as a human teacher who is teaching another human on a one-to-one basis, but the AI can, when well-designed, be as effective as a teacher teaching a whole class of students.


In addition to intelligent platforms and intelligent tutoring systems, there are many intelligent recommendation systems that can help teachers to identify the best resources for their students to use, and that help learners identify exactly what materials are most suitable for them at any particular moment in time. It is not just by learning particular areas of the curriculum that AI can make a big difference. AI can also help us to build our cognitive fitness, so that we have good executive functioning capabilities, so that we can pay attention when needed, remember what we learn and focus on what needs to be done. This system called MyCognition, for example, enables each person who uses it to complete a personal assessment of their cognitive fitness and then train themselves using a game called Aquasnap. AI helps Aquasnap to individualise training according to the needs of the particular person who is playing.


Finally, just in case you thought the reality of AI was only for adults, think again. This example from Oyalabs is a room-based monitor that can track the progress of a baby and provide that baby’s parents with individual supportive feedback to help them support their child’s development as effectively as possible.


That’s the reality of what’s available here and now when it comes to AI for education.


But what about the potential for the future? You’ll remember that I mentioned before that data can be described as the new oil and that it is the power behind AI. You heard Lewis talk about the way that data has been a ‘game changer’ for AI in education. And data can also be the power behind human intelligence. We can collect data in many, many ways, from our interactions with our smartphone to wearable technologies that track our heart rate, temperature, pulse, the speed of our movement and the length of our stillness. We can collect data about our interactactions with technologies in traditional ways, we can collect data passively through cameras that observe what is happening, we can collect data from technologies that are embedded in the clothes that we wear.


There are, of course, many important ethical implications associated with collecting data on this scale and these need to be addressed, but the scale of data collection is already happening and it is important to think about how this data could power education systems, not just systems designed to influence our spending or voting habits. If we accept the premise that data is the new oil and we are willing to invest the time in cleaning the data, then the final ingredient that we need to add, if we are to meet the potential that AI can bring to teaching and learning, is that we must design the AI algorithms that we use to process the data in a way that is informed by what we understand from research in the learning sciences, such as psychology, neuroscience, education. If we get this right then we can turn the sea of data that can be generated as people interact in the world into, an intelligence infrastructurethat can power all of the educational interactions of an individual.


This intelligence infrastructure can empower what we do with our smartphones, laptops, desktops, robotic interfaces, virtual and augmented reality interfaces and of course when we sit alone reading and working through books or when we interact with another person as part of our learning process. This intelligence infrastructure can tell us about how we are learning, about the process of learning, about where we are struggling and where we are excelling, based on extremely detailed data and smart algorithmic processing informed by what we understand about how people learn.


This intelligence infrastructure can also be used to power technologies to support people with disabilities and in so doing to help improve equality and social justice. We will be able to build intelligent exoskeletons, we can build intelligent glasses that can help the blind to see, and we will be able to tap in to what processing is happening in the brain allowing people to think what they want to happen on the computer screen and see it happen. But we need to remember as I highlighted earlier, that there are ethical Implications here. The potential for good is great, but unfortunately so is the potential for bad. Technologies that can be embedded in the body that can tap into the brain and bring a danger of what Noah Yuval Harare calls ‘hacking humans’.


So, what about the second implication of AI education? This implication is about educating people about artificial intelligence, so that they can use it safely and effectively. This tree diagram summarises the three key areas that I believe we need to educate people about when it comes to AI. We need everyone to have a basic understanding of AI, so that they have the skills and the abilities to work and live in an AI enhanced world. This is not coding, this is understanding why data is important to AI and what AI can and cannot achieve. We also need everyone to understand the basics of ethics, but we need a small percentage of the population to understand a great deal more about this so that they can take responsibility for the regulatory frameworks that will be necessary to try and ensure that ethical AI is what we build and use. And then there is the real technical understanding of AI that we need to build the Next Generation of AI system. Again, a small percentage of the population will need this kind of expert subject knowledge.


I would like to dwell for a moment on the ethical aspect of that tree diagram. There are many organisations exploring ethics and AI, or ethics and data. I find it useful when thinking about ethics to break down the problem into different elements. Firstly, there is the data that powers AI. Here, we need to ask questions such as: who decided that this data should be collected? Has that decision been driven by sound ethical judgement? Who knows that this data is being collected and who has given informed consent for this data to be collected and used? What is the purpose of this data collection, is it ethical.? What is the justification for collecting this data, is it sound? We must always remember that we can say no.


Next, we need to consider the processing that happens when the machine learning AI algorithms get to work. Have these algorithms been designed in a way that has been informed by a sound understanding of how humans learn? Have the AI algorithms been trained on datasets that are biased, or are they representative of the population for whom processing is being done?


And finally, there is the output – the results of the processing we have done through our AI algorithms. Is the output suitable to the audience? Is it genuine or is it fake? What’s happening when that output is received by the human interlocutor? Are we collecting more data about their reactions to this output?


There are many questions to be asked about the ethics involved in AI and education and here I have just scratched the surface, but it’s important to highlight that the ethical issues are extremely important. This is the reason I co-founded the Institute for Ethical AI and Education, because we believe that it’s an area that needs far more intention. We will be working towards the design of regulatory frameworks, BUT it’s important to remember that education will always be crucial, because regulation will never be enough on its own. We simply cannot keep up with those who want to do harm through the use of AI. We must therefore ensure that everyone is educated enough to keep themselves safe.


Finally, we come to the third category of implications from AI and education: changing education so that we can focus on human intelligence and prepare people for an AI world.


Many people, including the World Education Forum are telling us that we are now entering the Fourth Industrial Revolution – the time when many factors across the globe, including the way that AI is powering workplace automation, are changing the workplace and our lives for ever. There is much media attention to this Fourth Industrial Revolution with some coverage making such positive predictions as these from Australia that suggest that we will have two hours more time each week, because some of the more tedious aspects of our jobs will be automated. Our workplaces will be safer, and jobs will be more satisfying as we learn more.


Not everyone is as optimistic and there are an increasing number of reports that consider the consequences for jobs of the increased automation taking place in the workplace. This is an example from a report called ‘Will robots really steal our jobs’ published in 2018 by PWC. We can see from this graph from the report that transportation and storage appear to be the areas of the economy where most job losses will occur. Education will be the least prone to automation. We could interpret that as meaning that education will not change. However, I believe that education will change dramatically. It will change as we use more AI and it will change as what we need to teach changes in order to ensure that our students can prosper in an AI augmented world. And if we look at the second chart, it is perfectly clear that the impact will not be felt by everyone equally. Of course, those with higher education levels will be least vulnerable when it comes to automation and job loss. We therefore need to provide particular support for those with lower levels of education.


Personally, I do not think all these reports are that useful, interesting as they are. As a race, we humans are rather poor at prediction and the differences of opinion across the different reports indicates the complexity of predicting anything in such fast-changing circumstances. Trying to work out what to do for the best in a changing world, is a little bit like driving a car in dense fog along a road that you don’t know. In these circumstances, a map about the road ahead has limited use. What we really need is to know that we have a car that is well-equipped, we have brakes that work, lights that work. We want to be warm and we want to know that as a driver, we understand how to operate the car, that we understand the rules of the road, that we have eyesight that’s good enough to help us to see in the limited visibility ahead and that we can hear what is going on so that you can spot any impending dangers if they are indicating their presence by being noisy. A huge truck thundering towards us, for example.


So, what’s the equivalent of this good car and good driver when it comes to what we need in order to find our way through the fog of uncertainty around the Fourth Industrial Revolution?This is a subject that I have studied and written about quite a lot and a subject that is covered in this book: Machine Learning and Human intelligence: the future of education 21stcentury. Here I can only skim over the way that I unpack the intelligence that we need humans to develop if we are to find our way through this foggy landscape. This is the intelligence that can help us to cope with the uncertainty and it can help us to differentiate ourselves from AI systems. This is an interwoven model of intelligence that has seven interacting elements:


The first element of this interwoven intelligence is: interdisciplinary academic intelligence. This is the stuff that is part of many education systems at the moment. However, rather than considering it through individual subject areas as we do now, we need to consider it in an interdisciplinary manner. Complex problems are rarely solved through single disciplinary expertise, they require multiple experts to work together. The world is now full of complex problems and we need to educate people to be able to tackle these complex problems effectively. We therefore need to help our students see the relationships between different disciplines. We need them to be able to work with individuals who have different subject expertise and to synthesise across these disciplines to solve complex problems.


Secondly, we need to help our students understand what knowledge is, where it comes from, how we identify evidence that is sound enough to justify that we should believe that something is true. I refer to this as meta knowing,but of course we can use the terminology of epistemology and personal epistemology to describe this meta knowing.


The third elements of our intelligence that we really need to develop in very sophisticated ways is social intelligence. It is very hard for any artificially intelligent systems to achieve social intelligence, and it is fundamental to our success. Because, we need to collaborate increasingly in order to solve the kinds of complex problems that we will be faced with on a daily basis.


Fourthly, we need to develop our meta cognitive intelligence. This is the intelligence that helps us to understand what we need to know to understand how we learn, how we can control our mental processes, how we can maintain our focus and spot when our attention is skidding away from what it is we are trying to learn. These metacognitive processes are fundamental to sophisticated intelligence and they are again hard for AI to achieve.


The fifth element of intelligence we must consider is our meta emotional intelligence. This is what makes us human. We need to understand the subjective emotional experiences we sense and we need to understand the emotional perspectives of the others with whom we interact in the world. This emotional intelligence is also hard for AI.  AI can simulate some of this, but it cannot actually feel and experience these emotions.


We also need to recognise the importance of our physical presence in the world and the different environments with which we interact. We as humans, are very good at working out how to interact intelligently in multiple different environments. This meta contextual intelligence is something at which we can excel, and something that AI has great trouble with. Context here means more than simply physical location it means location, it means the people with whom we interact, the resources that are available to us and the subject areas that we need to acquire and apply in order to achieve our goal.



If we can build these interwoven elements of our human intelligence then we can really achieve what’s important for the future of learning and that is: accurate perceived self-efficacy. By this I mean that we can see how we can be effective at achieving a particular goal, at identifying what that goal consists of, identifying what aspects of that goal we believe we can achieve now, what aspects we need to learn about and train ourselves to achieve. In order to be self-effective, we must understand than to apply all the elements of intelligence so that we can work across and between multiple disciplines with other people with effective control and understanding of our mental and emotional processes.


Let me take a moment to stress something important here.  This is about intelligence. It is not about 21stcentury skills or so-called softskills. It is about something much more foundational than any skill or knowledge. It is about our human intelligence. I also want to emphasize that we can measure the development of our intelligence across all seven elements. They can all be measured and importantly they can all be measured in increasingly nuanced ways through the use of AI. This enhanced and continual formative assessment of our developing intelligence will shed light on aspects of intelligence and humanity that we have not been able to evidence before. We can use our AI, to help us to be more intelligent, and this is very important.


The truth of the matter is that being human is extremely important. The very aspects of our humanity, the aspects that we do not measure, but that are fundamental to what it means to be human, are the ones that we are likely to need more of in the future. For example, empathy, love and compassion. If I ask you to look at these pictures – what do you feel?


In the words of Yuval Noah Harare from 21 Lessons for the 21stCentury: …” if you want to know the truth about the universe, about the meaning of life, and about your own identity, the best place to start is by observing suffering and exploring what it is.”


What AI can or will ever be able to do this? It is important that we ensure that we still can.


And now, if we look at these pictures – again, I ask you what do you feel? How do those feelings impact upon the way you might behave? We undervalue these aspects of humanity when it comes to our evaluations of intelligence, and yet I would suggest that it is our human emotional and meta intelligence that enables us to feel horror at human suffering and pleasure at human love.


The holistic set of interwoven intelligence enables us to be human and AI can help us to both develop the sophistication of our intelligence, across all its elements, and it will also help us to assess, and yes, to measure much of this. If that is what we want to do. What we need is good data, and smart AI algorithms that have been designed in a way that is informed by what we know about how humans learn.


We can collect data from a vast array of sources these days. We can collect it as people interact in the world, even when they do not realise, they are interacting with technology. We are no-longer simply restricted to collecting data explicitly through the interfaces of our desktop, laptop, tablet and smart phone technologies. We can collect it through observation, wearable technology and facial recognition.


For example, in research conducted at the Knowledge Lab with my colleague Dr Mutlu Cukurova, we collected a range of data in our attempts to understand how we could identify signifiers of collaborative problem-solving efficacy that would be susceptible to AI collection and analysis. As you can see from this visualisation of all the data sources, we were able to capture, it is complex and does not tell us anything of great interest.


However, by using learning from the social sciences that provides evidence that factors, such as the synchrony of individual group members’ behaviours can signify positive collaborative behaviours. We can then inform the design of the AI algorithms that we use to process this data. We therefore analysed the eye tracking and hand movement data that we collected through this research test rig. We found that there was indeed a greater instance of synchrony of eye gaze and hand movements between different members of a group, when that group was behaving in a positively collaborative manner, as assessed by an independent expert.


This is just one example of a small signifier, but when combined with a battery of other detailed signifiers, we can start to generate accurate and nuanced accounts of what is happening as people learn. Accounts that can be extremely useful to teachers and learners. AI can help us to track and support the development of our human intelligence in very sophisticated ways.


But, what does this mean for teaching?

Numeracy and literacy, including data literacy, will of course remain fundamental to all education, as will the basics of AI;

Emphasis for the remaining subject areas needs to be on what these subjects are, how they have arisen, why they exist, how to learn them and how to apply them to solve complex interdisciplinary problems;

Debate and Collaborative Problem Solving provide powerful ways to help students understand their relationships to knowledge and to hone their ability to challenge and question;


To ensure that teachers and trainers have the time to work with their students and trainees to develop these complex skills, we can use AI to:

Provide tutoring for numeracy, literacy (including data literacy) and basic subject knowledge;


We then blend this with our human-intelligent teachers who can refine this understanding through activities such as debate and collaborative problem solving; and to develop learners’ social and meta intelligence (meta-cognitive, meta subjective, meta contextual and accurate perceived self-efficacy);


And then the finesse to this piece – we use the AI to analyse learner and learning data, so that teachers know when to provide optimal support and learners get to know themselves more effectively.


I find that decision makers in education are very risk averse and often do not want to make big changes, because they are concerned that such changes might disadvantage the people in the process of their education when the change hits. I can understand this. However, if we do not make big changes the consequences are likely to be worse, and the risks much greater. As the FT expressed this in 2017:


“The risk is that the education system will be churning out humans who are no more than second-rate computers, so if the focus of education continues to be on transferring explicit knowledge across the generations, we will be in trouble.” (Financial Times 2017).


This would be a retrograde step indeed and would take us back to the first instances of robots, as seen here in this image from a play by Czech writer Karel Čapek who introduced the word robot in 1920 to describe a race of artificial humans in a futurist dystopia.


To sum things up as we draw to a close: We need to make these three things happen: Use AI to tackle educational challenges, prioritize the development of our uniquely human intelligence, educate people about AI. To do this we need partnerships between educational stakeholders to build capacity.


Partnerships of the type that we build through the EDUCATE programme. These partnerships are to generate the golden triangle that is the foundation of impactful high-quality educational technology (including AI) design and application. The idea of the Golden Triangle formed at a meeting in January 2012 when I was talking with my research colleagues Mike Sharples and Richard Noss, along with Clare Riley from Microsoft and Dominic Savage from BESA – we had met up with some educators and were puzzling about why the UK Educational Technology business was not better connected to researchers and educators. This was the birth of the triangle: the points of which are the EdTech developers, the researchers and the educators – all of whom need to be brought together to develop and apply the best that technology can provide for education.


The triangle is golden, because it is grounded in data. It is a tringle, because it connects the three key communities: the people who use the technology, the people who build the technology and the people who know how to evidence the efficacy of the technology for learning and/or teaching.


This golden triangle is at the heart of what needs to be done for AI to be designed and used for education in ways that will support our educational needs. It is the triangle at the heart of the partnerships that engage the AI developers, most of whom do not understand learning or teaching, with the educators, most of whom do not understand AI, and the researchers who understand AI and learning and teaching. It is the co-design partnership that will drive better AI for use in education, more educated educators who can drive the changes to the way we teach and learn that are required for the fourth industrial revolution and also help their students understand AI, and more educationally savvy AI developers


The Reality and the Potential of AI is that:


AI is smart, but humans are and can be way smarter.


There are 3 ways AI can enhance Learning and Teaching

  • Tackle Educational Challenges using AI;
  • Prioritize Human Intelligence;
  • Educate people about AI: Attention to Ethical AI for Education is essential;


Partnerships are the only way we can achieve this.


Many thanks for coming to hear me speak this evening. I hope that I have piqued your curiosity and that you will have many questions to ask me.


Malala Yousafzai’s A level results are brilliant, we need more successes like this

Who could be anything but delighted to see this headline? A-level results: Malala Yousafzai gets a place at Oxford, this is excellent news and a great boost for those campaigning for equal education. In fact, the publication yesterday of A level results in the UK has spurred me to take a slight diversion from worrying about who is moving my brain or my cheese. I certainly would not want to detract from the hard work that any students have put into their A level studies or to take the shine off their success. It is wonderful to see the smiling faces of successful students across the newspapers.

However, success does not come to all and even on a celebration day, or perhaps I should write especially on a celebration day, I think we need to consider alternatives to the stressful stop and test regime that pervades most education systems. I wrote about this in Nature Human Behaviour earlier this year under the heading: ‘Towards artificial intelligence-based assessment systems’ and it looks like it has been read a few times because it is ranked 5,746th of the 237,966 tracked articles of a similar age in all nature journals which puts it in the 97th percentile. This does not seem bad given that it was only a ‘comment’ piece and not a full paper. On a less positive note in an internal REF assessment exercise it was only ranked as 2*, which is not great and probably reflects the difficulty for academics in publishing more popular style articles. However, the modest success of the article in terms of the altometrics that Nature run encourages me to believe that there is some interest in exploring the possibilities that the intelligent design and application of AI could afford for National assessment systems. I therefore draw attention to this possibility here and hope to encourage further debate. The key point I wanted to convey in the Nature Human Behaviour article was that there are alternatives to exams, that are less stressful, less expensive and that allow teachers and learners to spend more time on teaching and learning (shouldn’t this be the point of education?).

This message may not be what others have selected to focus on, but for me, the most important thing is that we have an assessment system that is holistic, fair and that let’s all students evidence their knowledge, skills and capabilities.


Who moved my brAIn? Is this the next self help best seller to get us ready for our AI future?

I’m pleased to report that my ankle is progressing well and I am now once again able to achieve my ‘misfit‘ challenge of 1000 activity points per day: clearly it is a good job I was only mildly curious. However, I want to be more than mildly curious when it come to my intellect, and I want to do this without injury. I had therefore better take care, both of my own intellect and of the intellect of those I am trying to encourage to be appropriately curious. I therefore return to my thoughts about what a useful self-help book to prepare people for their AI augmented futures, might be like. To this end, I also return to ‘The AI Race‘ and specifically to the man behind the survey that was used to calculate how much of different people’s jobs are likely to be automated.


Andrew Charlton is his name and he is economist and director of AlphaBeta, an economics and strategy consulting firm. He did not beat about the bush! AI will impact on ALL jobs and he encouraged the TV audience to embrace AI. His ‘top tip’ was that we must carefully manage the transition from now to the situation when widespread AI augmentation will be common place.

He was clear that we must take advantage of what AI has to offer by increasing the diversity of our own skills sets. He saw AI as an “Iron Man Suit” for humans. This suit would transform us mere humans into super humans. This is a great analogy, who would not want to be super human? BUT embracing AI augmented working is not as simple as putting on a new outfit – especially an iron outfit. And increasing the diversity of our skill sets requires educators and trainers who are themselves skilled and trained in developing these new diverse skill sets. BUT where are these educators and trainers to be found? Who is helping the educators and trainers to gain the skills and expertise they will need to train their students in?


Andrew has little comfort to offer here.  His next comment about education is that 60% of the curriculum that students are studying at school is developing them for jobs that will no longer exist in 30 years-time. We need to re-design the curriculum he advises. So educators need to re-skill themselves as well as their students, and they need to revise the curriculum. Clearly educators will be busy! And clearly there will also be a significant job to be done in (re-)motivating all those students who discover that they have been learning stuff that nobody will want them to know by the time they are looking for a job.

Now we hit the nub of the matter, education and educators must prepare students for the new AI order of things. Educators lives are going to change in significant ways NOT because their roles are likely to be automated away BUT because they will need to teach a different curriculum and probably teach in a different way. To make matters worst: there is no clear consensus from the experts about exactly which jobs educators will need to educate people for. I think educators may be the most in need of a good self-help book to help them cope with the inevitable changes to their lives.


The self-help book I need to write might therefore be the updated version of the motivational best seller Who Moved My Cheese? An Amazing Way to Deal with Change in Your Work and in Your Life. I suggested this might become ‘Who moved my brAIn? An Amazing Way to Deal with ChAnge in Your Work and in Your Life’


The original book called ‘Who Moved My Cheese’ was a story featuring 4 characters: two mice, called “Sniff” and “Scurry,” and two little people, called: “Hem” and “Haw.” These 4 characters all live together in a maze through which they all search for cheese (for cheese think – happiness and success). Their search bears fruit when all of them find cheese in “Cheese Station C.” “Hem” and “Haw” are content with this state of affairs and work out a schedule for how much cheese they can eat each day, they enjoy their cheese and relax.


“Sniff” and Scurry” meanwhile remain vigilant and do not relax, but keep their wits about them. When horror of horrors there is no cheese at Cheese Station C  one day, “Sniff” and Scurry” are not surprised: they had seen this coming as the cheese supply had diminished and they had prepared themselves for the inevitable arduous cheese hunt through the maze and they get started with the search together straightaway. In contrast “Hem” and “Haw” are angry and annoyed when they find the cheese gone and “Hem” asks: “Who moved my cheese?” “Hem” and “Haw” get angrier and feel that the situation they find themselves in is unfair. “Hem” is unwilling to search for more cheese and would rather wallow in feeling victimized, “Haw” would be willing to search, but is persuaded not to by “Haw”.


While “Hem” and “Haw” get annoyed, “Sniff” and “Scurry” find a new cheese supply at “Cheese Station N,” and enjoy a good feast. “Hem” and “Haw” start to blame each other for their lack of cheese. Once again “Haw” suggests they go and look for more cheese, but grumpy “Hem” is frightened about the unknown and wants to stick with what he knows, he refuses to search. However, one day “Hem” confronts his fears and decides it is time to move on. Before he leaves “Cheese Station C” he scribbles on the wall: “If You Do Not Change, You Can Become Extinct” and “What Would You Do If You Weren’t Afraid?” He starts his trek and whilst he is still worried, he finds some bits of cheese that that keep him going as he searches. He finds some more empty cheese stations, but also some more crumbs and is able to keep hunting. “Haw” has realized that the cheese did not simply vanish, it was eaten. He is able to move beyond his fears and he feels ok. He decides that he should go back to find “Hem” equipped with the morsels of cheese he has found. Sadly, “Hem” is still grumpy and refuses the cheese morsels. Undeterred, though somewhat disappointed, “Haw” heads back into the maze and a life of cheese hunting. He continues to write messages on the wall as a way of externalizing his thinking and in the hope that “Hem” might one day move on and be guided by these messages. One day “Hem” finds Cheese Station N with all its lovely cheese, he reflects on his experience, but decides not to go back to “Hem”, but rather to let “Hem” find his own way. He uses the largest wall in the maze to write the following (original to the left, my re-interpretation to the right):

Who move my cheese? Who moved my brAIn?
Change Happens: They Keep Moving The Cheese Computers keep getting smarter and intelligent tasks are moving from human to machine
Anticipate Change: Get Ready For The Cheese To Move Prepare for some of your intellectual activity to be taken on by AI
Monitor Change: Smell The Cheese Often So You Know When It Is Getting Old Keep checking in on your own intelligence and make sure you are really using it and keeping it fresh
Adapt To Change Quickly: The Quicker You Let Go Of Old Cheese, The Sooner You Can Enjoy New Cheese Adapt to change thoughtfully (quickly is not necessarily right here), make sure you offload intellectual activity carefully so that you maintain your human intellectual integrity
Change: Move With The Cheese Move with the intelligence (both human/natural and machine/artificial)
Enjoy Change!: Savor The Adventure And Enjoy The Taste Of New Cheese! Enjoy intelligence and the experience of your developing greater intelligence – being smart ‘tastes good’!
Be Ready To Change Quickly And Enjoy It Again: They Keep Moving The Cheese Never feel you are intelligent enough and keep striving for intellectual growth

“Haw” is never complacent and continually monitors his cheese store and searches through the maze and hopes that one day his old friend “Hem” will find his way through and that they will meet again.


Whilst the book “Who moved my cheese” was extremely popular, it was also the subject of considerable criticism. For example, that it was too positive about change, that it was patronizing and compared people inappropriately to ‘rats in a maze’. BUT can I learn anything from this as I try to encourage people to want to understand themselves and their changing intellectual capabilities?

I think there is still value in “Haw’s” writing on the wall and I have tried to clarify this nee value for AI in the right hand column of the table above. I also think perhaps that my original revised title of: “Who moved my brAIn?” is not quite correct. The more important question is “Who moved my intelligence?”.

More to come on this in the next blog post…


AI is our future, but can we convince Frank?

As a child I was always frustrated by the phrase: “curiosity killed the cat”. This was a frequent retort when I was trying to understand how things worked. Well, I am not reporting any cat killing incidences here, but my curiosity about myself driven by my new ‘misfit’ may have been a primary factor in my newly sprained ankle!


Over enthusiasm to meet that target of 1000 activity points motivated me to get walking and launched me down some steps in a most ungainly and unfortunate manner.  No broken bones, but some swelling and plummy bruising have resulted in my needing to rest up for a few days. Resting up in a Sydney winter is hardly a chore, the sun is out and the sky is blue and I indulged in exploring the ABC TV channel and in particular a great program called The AI Race.

The AI Race

The program presented data from a study into the risks to Australian jobs from AI powered automation. I was relieved to see that professors are only likely to have 13% of their job automated, whilst carpenters are predicted to have 55% of what they do done by smart technology. Might this be the same in the Uk, or different I wondered? The ABC reporter explored various jobs and met up with employees. For example, Frank: a truck driver, was not persuaded that autonomous trucks would be able to replace his experience and intuition about the behaviour of other humans whether pedestrian or driver. The autonomous vehicles would not be able to help out other drivers stranded on the roadside or provide human customer service on delivery of a load either. He was definitely not convinced that AI was going to replace him any time soon.


Further jobs were explored: the legal profession for example where law students were stunned by an AI para legal that could search through thousands of documents to find a specific clause in no time at all. The law students berated their education for not preparing them for a world of automation.


On the one hand we have Frank, who does not believe that AI can replace him, and on the other we have a group of law students who are persuaded that AI can already do a lot of what they are studying to be able to do. Nobody seems very curious about how they might better prepare themselves for AI’s onslaught on their workplace. So, how might I persuade them that understanding more about their own intellect could help them work more effectively with AI? The key to future success has to be that people need to focus on developing the expertise that AI cannot achieve: the still unique human qualities that will be at a premium. Self-knowledge and Self-efficacy are important elements of this expertise, but how do we motivate people to develop themselves? To start answering this, I looked at the best selling self-help books for guidance. People buy these so maybe I can learn something about how to appeal from their sites – which of these might work best?

Who moved my brAIn?

What colour is your AI?

How to win with AI

7 AI habits of effective people

I’m AI, you’re ok

Rich augmented me, poor augmented me

AI is from Silicon, we are from the Gene Pool

I’m not convinced about any of these…….

AI and personal analytics provide a ‘fitbit’ for the Intellect

It is far too long since I last posted to this blog: too many jobs and too little time would be my fist attempt at an excuse. But, perhaps it is just that I am not effective enough, that I need better self-regulatory skills, more intelligence and a better understanding of my own strengths and weaknesses. I talk quite a lot about intelligence and about how AI developers have not yet designed artificially intelligence systems that understand themselves and have metacognitive awareness, but maybe I too lack these abilities? So, how might I become more self-effective?

This thought is one that I intend to worry at while I am completing a research trip to the University of Sydney to work with my colleague Judy Kay. We are working on Personal Analytics for Learners (PALs), or more precisely interface designs for PALs (or iPALs).

In order to help me thing this through I wanted to learn more about some of the work that Judy and her colleague Kalina Yacef have been doing in collaboration with medics and health professionals to develop better data analytics and interfaces for personal health information for education. For example, the iEngage project provides a digital platform for children with information, education and skills to help them to achieve their physical activity and nutritional goals. It connects with ‘misfit‘ activity trackers to provide continuous feedback and summarise the daily activity on a dashboard.

To this end, I bought myself a ‘misfit’: a somewhat cheaper version of a ‘fitbit’ with a great name :-). I am now tracking my sleep and my pulse as well as my physical activity and diet in order to try and understand more about my personal wellbeing. This is nothing new and millions of other people do this too. I notice that popular technology stores stock a good range of fitness tracking devices and increasingly more reasonable prices.

IMG_4190  IMG_4191.jpg

So, in order to also help me be better at understanding my mind and my cognitive progression and metacognitive skills and regulation, I now need a ‘mindset” to help me track how well I am thinking, learning and regulating my working and learning. The interface to such a ‘mindset’ is the idea behind the iPAL that Judy and I are currently designing. I find it interesting to speculate about the kinds of data that we could collect about our intellectual and social interactions that would help us track and better understand our intellectual mental wellbeing as well as our physical welding and fitness. This kind of ‘fitbit’ for the mind might help me to be less distracted by non-priority activities and spend more time on priorities, such as writing.

A search for ‘fitbit for the mind’ yields some hits, though not terrifically interesting ones. There is an article in new scientist about eye-tracking to tell you more about your reading habits, and a mindfulness app that can be linked to fitbit data. The problem here is that we are being offered some automatic tracking of just one type of mental activity – reading, or mindfulness and actually we need something way more sophisticated to tell us about how we our intelligence and self-awareness is progressing. Perhaps something that looks at multiple data sources and provides us with an overview of our activity in a way that motivates us to want to know more about our intellectual fitness in the same way that activity trackers help us understand more about our physical fitness.

Earlier this month, there was a more interesting article in Newsweek that talks about ‘iBrain’ and the possibility for us to be able to track our brain’s electrical output and see markers for the likely occurrence of a range of mental health disorders from anxiety, depression, and schizophrenia, to dementia and Alzheimer’s before symptoms appear. Such information might help early intervention and monitoring. This reminds me of the rise of personal DNA services, such as 23 and me. If people are interested in their DNA and what it might tell them about how they should adjust their lifestyles to avoid certain conditions that they look to be susceptible to, then maybe people are also curious about their intelligence and how they can understand it better.


Over the next few blog posts I plan to explore what such a device might be like, what data it might collect and how I might best benefit from the sorts of information it could provide.

Truth, Lies and Enlightenment: how AI can help us to build knowledge and understanding in the echo chambers of life

AI is both a cause and a solution to the problem of a world where there is far more information than any one person can possibly effectively process to construct their own understanding about what they believe and what they don’t. AI can amplify the echo chamber by promoting the most believed over the most evidenced. BUT it can also help us to recognize valid information from noise, IF we know the right questions to ask and IF WE KNOW HOW TO WORK WITH OUR AI we can develop deep understanding and escape from the maze of invention…

Early in my career I was advised that if I wanted to get a point across when teaching, during an interview, as part of a presentation or when debating, I must repeat the point I wanted to make three times. There is an empirical basis for this advice: something eloquently explained my Malcolm Gladwell and the motivation for my blog identity: The Knowledge Illusion. Put simply, when people are provided with more information about X, they believe that they know more about X, when in fact they often know less about X. I wrote about this many blogs ago (transcribed below for ease of reference) to draw attention to the essential need to help people decipher the huge volume of information that comes their way so that they can discern what is genuine from what is fake.

I still follow the “say things three times” advice in my endeavour to communicate what I consider to be valid, some might say truthful, information. My objective is to persuade people that my perspective, opinion, or information presentation is the stuff to be believed. However, I accept that it is entirely up to my audience to decide whether or not they are won over. The importance of this subjective experience and the belief that an audience are actively analysing the information that comes their way is ever more important. In a world of echo-chambers and deluge of social media, we need people to be able to look at a stream of data and information and make intelligent decisions about what they believe to be the stuff of knowledge.

The problem is not new. It was JFK who once observed that “No matter how big the lie; repeat it often enough and the masses will regard it as the truth.” This is an enormous insult to the intelligence of the “masses”, but unless we pay attention to helping these “masses” to navigate through the morass of mediocracy that social media precipitates, proliferates and perpetuates then we will return to the pre-enlightenment era when the world was flat and knowledge was the privilege of those who knew how to decipher the written word and who acted as the mouth-piece for and the collective intellect of their communities: the “masses”.

The word “masses” is no longer widely used so let’s just refer to the “masses” as the people: the global human race whom education is intended to equip with the skills and abilities to think and make sense of the world and the information others produce about it. To consider what it is we need to do to help people to make sense of the world it is worth travelling even further back in time to the views of Roman Emperor Marcus Aurelius that: “Everything we hear is an opinion, not a fact. Everything we see is a perspective, not the truth.” We need to encourage a nuanced belief system where people are provided with the skills, confidence and resources to construct their own understanding from the tidal wave of data and information that threatens to engulf them.

Again, history can help to inform us. The scientific revolution set the stage for the age of enlightenment that transformed the human race and promoted the importance of reason. Influential thinkers like Bacon, Locke and Descartes paved the way for the likes of Voltaire, Kant and Smith. Life was so much simpler then of course, but the huge increase in what it is possible for an individual to try to understand and know does not discount the important role that influential thinkers can play.

The birth of the www and social media represent a new generation of publications that play the role of the encyclopedias and dictionaries in the age of enlightenment. BUT who are the key philosophers and scientists who can catalyze the popular debates in the way that the philosophers of the enlightenment did? Stephen Hawking would probably be high on the list of influential thinkers who many people (the “masses”) might be able to name. Who else?

Whilst the volume of information and data about the world has ballooned, the number of influential thinkers who can help people find their way to knowledge and understanding has may not have kept pace. Technologies that harvest the ‘wisdom’ of the crowd often promote the loudest shouters and the most-followed, rather than the considered and grounded reasoning of the real intellectuals. The demise of expertise has exacerbated the problem as professional predictions have failed to materialize…. Let’s just stop there for a moment.

Could the real problem be that we, the people, don’t know how to interpret expertise? We want simple answers when there are none to be had. In schools we still encourage the belief that rote learning and subject specific information of the type that can be reproduced by a single person when challenged with a standardized test sufficient. This outdated approach gives the impression that knowledge and understanding are way more simple than they really are. They encourage people to believe that there is a body of stuff that they need to learn and reproduce, and that if they can do this they will be knowledgeable. However, what we should be doing is ALSO encouraging people to constantly probe, prod, compare and conclude for themselves their understanding of the world so that they can apply this knowledge to solve the problems they encounter every day.

The surge of tweets that give the impression that meaningful things can be said in 140 characters is not always helpful either. There is certainly something to be said for trying to distil understanding into a short text — it is difficult and can test how much we really understand. However, the believe that a tweet can be the whole story in and of itself is misguiding. Knowledge and wisdom need to be worked at, by questioning, analyzing, aggregating and synthesizing to reach our own evidence-based beliefs about what we know and what we understand. Someone else’s tweet might start this process, but we have to finish it for ourselves.

Ai can help us to do the work here. AI can analyze and visualize complex data and information in order to literally help us see the ‘wood from the trees’. AI can be built to model human understanding and to justify the decisions and predictions that it makes. AI can explain to us how to complete complex activities, such as solving mathematical equations or managing a complex power plant. BUT Artificial and Human Intelligence must work together to help people extract the truth from the lies. We as humans must ensure that we know enough about what AI is capable of doing to ensure that we ask the right questions. We must learn to be discerning enough to challenge the AI when we are not convinced by what it is telling us.

This means that now more than ever we must educate the educators. Because educators must instill in us, the people, the investigative skills that we need to ask the right questions so that we can differentiate evidence from falsehood. Educators must encourage the confidence and self-efficacy in us that will help us believe our own minds. Educators must engender the perspective taking and integrative thinking that will enable us to work together to solve problems and to develop the influential thinkers we need now more than ever to enlighten us.

More relevant than ever…Information plenty, but knowledge famine: are we succumbing to an illusion?

I am curious about knowledge, not in philosophical sense, but in a practical one. I worry about what it means to know something in a world that is increasingly complex, ill defined and interconnected: a world that demands that we develop, and that we ensure that our children develop, the knowledge capacity to solve the problems it manifests and those that we create.

The first recollections that I have of my own curiosity about knowledge date back to 1966 when I was eight years old and growing up in Manfred Mann’s semi-detached suburbia: dad, mum, older brother and me. My father was an aircraft engineer and my mother taught typing and shorthand to women whose working lives were about to be dramatically changed by the word processing power of the digital computer. My brother was 3 years older than me, and his lack of interest in formal education was causing my parents some concern. Their reaction was to invest in ‘knowledge books’, or at least that’s how they saw the children’s book of knowledge and the encyclopedia that now filled up the bureau bookshelf. To keep us up to date, there was also the weekly general knowledge magazine that plopped on the doormat with a reassuring thud: the weight of its knowledge there for all to hear.

I suspect that my parent’s reaction to their son’s educational malaise was not an unusual one amongst the aspiring middle class families of our neighbourhood. My brother’s reaction to the new literary arrivals was cool; he was far more concerned with exploring the world of the woodland around our housing estate, than with sitting at home and reading about it. My father however, became quite addicted to the weekly general knowledge magazine. He did not have a great deal of time to read, but each evening when he went to bed he would sit in his paisley pyjamas and thumb through the pages. The stock of copies soon grew on the nightstand as his pace of reading failed to match the frequency of their arrival. The corners became slightly curled as the months and years passed and the dust gathered in and around the pile that now extended from the nightstand to the floor. His interest never waned and I do believe there were a pile of old issues by his bedside when he died many years later.

Forty years on and it’s a sunny day and I’m walking along the Euston Road in London. I pass the entrance to the British Library and a sign catches my eye, the sign says: “Step inside – Knowledge freely available”. I dislike the suggestion that one can walk into the British Library and just pick up some knowledge like going into Tesco and buying some bananas. I can relatively quickly formulate an explanation for myself about why the sign irritates me, because I have a clear idea about what I believe knowledge to be. I have moved on from the conception of knowledge loved by my father and represented by the pages of his books and articles. I know that I have to construct knowledge from the evidence available to me, that it is not handed to me by others, though they can certainly help me along the way, and that I can aspire to continually increase my knowledge by weaving together the information resources distributed throughout my world.

This is not the case for many of the youngsters who attend our schools and colleges. For them knowledge is still to be found in the dusty concepts in the out of date magazines on my father’s nightstand or on the shelves of a library they never visit.

“But what of the internet and world wide web?” I hear you wonder. These technological masterpieces offer information resources wherever we are and whenever we need them. These must surely pave the way for us to become more knowledgeable, both personally and as a human community?

The sheer abundance of this information has thrown into sharp relief our understanding of the relationship between information and knowledge. It makes my modest collection of childhood encyclopedias and my father’s overflowing magazine collection look like a speck of dust on the library shelf. I fear however that our understanding of what knowledge is and what it means to know something has not progressed in tandem with this technological progress. This puts us at risk of succumbing to the illusion that we know more than we actually do, because the more information we have the more we become certain that we know something.

Without helping young people to develop an understanding of what knowledge is in a digital age they cannot progress beyond the well meaning, but limited conception of knowledge promoted by the books and magazines that appealed to my parents. Those of us who understand what we mean by knowledge can indulge ourselves, as my father did with his magazines. But, without actively engaging people in the excitement of connecting the knowledge construction process to their own particular context, we merely encourage them to pass the opportunity by in the same way as my brother did all those years ago.

In a time of information plenty we are at risk of a knowledge famine.

I wrote thsi piece originally for  Learning to Live – Creativity, Money and Love

We have the technology to eradicate exams, tests and stress forever, so why aren’t we using it?

The recent leaking of SAT papers and the growing body of evidence on the stress and anxiety experienced by students who have to sit a battery of tests and exams highlight an area of serious concern. It is all particularly frustrating because it does not have to be like this.

Artificial Intelligence (AI) could wipe out all this pain and change schools forever: it could do away with the need for exams.

This is not to suggest that we should do away with Assessment. It is essential that we know how students are progressing in their knowledge, understanding and skills, and how teaching practices and educational systems are or are not successful. However, assessment does not have to mean tests and exams.

exam_stressArtificial Intelligence is difficult to define because it is constantly shifting and interdisciplinary. However,  AI systems can be described as computer systems that have been designed to interact with the world through capabilities (for example, visual perception and speech recognition) and intelligent behaviours (for example, assessing the available information and then taking the most sensible action to achieve a stated goal) that we would think of as essentially human.[1]

AI has been in the news recently with the AlphaGo programme beating a human champion Go player for the first time and the prospect that Google’s driverless car will soon be available for us to try (). On the negative side there are concerns about the impact of increasingly sophisticated AI on our economy and in particular the jobs market.


However, the sort of AI I am talking about here is specific to education and has the catchy acronym AIEd. It has been the subject of academic research for more than 30 years and promotes the development of adaptive learning environments and other tools that are flexible, inclusive, personalised, engaging, and effective. At the heart of AIEd is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.”[2] In other words, in addition to being the engine behind much “smart” EdTech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper and more fine-grained understandings of how learning actually happens.


Artificial Intelligence tools and techniques could do away with the need for stop and test assessments and all the stress and anxiety that goes with them. There would be no more need for marking and re-marking, no appeals about results, none of the machinery of exam sitting that dominates the summer term in secondary schools with its “Silence, exam in progress” signs and the commandeering of sports facilities for use as exam halls. There would be more time for teaching, more time for sport and more time for curriculum enrichment.


AIEd provides the technology to conduct fine-grained analysis of learners’ skills and capabilities as they learn so that their developmimages-1ent can be tracked continuously and appropriate support provided. Instead of traditional assessments that rely upon evaluating small samples of what a student has been taught, AIEd-driven assessments could be built into meaningful learning activities, perhaps a game or a collaborative project, and will assess all of the learning (and teaching) that takes place, as it happens[3]. AIEd also offer the capability to track the 21st Century Skills that the modern workplace requires and that traditional assessment miss. Skills such as critical thinking, collaboration and initiative.

There is of course a considerable commercial ecosystem surrounding the current assessment system and this may cause some hesitation about adopting the AIEd continuous assessment and support approach. There are also significant ethical issues that need to be considered, such as who has access to the data-stream about student performance and can it be edited or commented on by parents, teachers or the student. The adoption of an AI driven assessment system would be a huge cultural change and not everyone would understand it or feel comfortable with it. Many innovations do not meet with immediate popularity: electric vehicles for example, but over time they are accepted, their benefits are appreciated and their popularity grows.



Unfortunately, there is a hesitation in the UK to exploit either the social and economic potential of AIEd or its commercial benefits. Funding is poorly targeted and as a consequence the UK is at risk of losing its internationally leading research base and its competitive edge. We need to move from the cottage industry of existing UK AIEd research, to a rich ecosystem of disciplined innovation. And we need to move from siloed and short term funding to a funding landscape that reflects AIEd’s enormous potential.


But, most importantly of all we need to engage teachers and learners, employers and workers, in the design of the AIEd systems that are developed to provide both the assessment and the learning benefits that this technology has to offer.


This blog post can also be found on the UCL IOE blog. It draws on the following publication, where readers can find out more about AIEd:


[1] ODE: The Oxford Dictionary of English (Oxford Dictionaries online). Oxford University Press, Oxford (2005) AND Russell, S.J., Norvig, P., Davis, E.: Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River (1995).

[2] Self, J.: The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education (IJAIEd). 10, 350–364 (1999).

[3] Hill, P. and M. Barber (2014) Preparing for a Renaissance in Assessment, London: Pearson.; DiCerbo, K. (2014). Why an Assessment Renaissance Means Fewer Tests.

What the Research Says about How AI Benefits Education

Thursday 24th March at 1pm
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Through a mix of presentations and discussion we will explore the evidence about if and how Artificial Intelligence (AI) can support teaching and learning. For those who would like to know more about AI and Education please see the report published earlier this month.

Click to access Intelligence-Unleashed-Publication.pdf

Speakers include:
Prof Benedict du Boulay – University of Sussex
Dr Wayne Holmes – Zondle
Dr Kaska Porayska-Pomsta – UCL Knowledge Lab
Prof Gautam Biswas – Vanderbilt University, USA
Dr Manolis Mavrikis – UCL Knowledge Lab

Junaid Mubeen – Whizz Education


WhenThursday, March 24, 2016 from 1:00 PM to 4:00 PM (GMT) Add to Calendar WhereUCL Knowledge Lab – UCL Institute of Education. 23- 29 Emerald Street . London, London WC1N 3QS GB – View Map

Thursday, March 24, 2016 from 1:00 PM to 4:00 PM (GMT) Add to Calendar
UCL Knowledge Lab – UCL Institute of Education 23- 29 Emerald Street , London WC1N 3QS, United Kingdom – View Map

Educational AI can know about teaching, learners, and subjects like math or history.

I said in AI in Education provides smart knowledge modeling tools that AIEd systems build computational models of the process of teaching, the subject matter being studied and of the learner as they progress. The table below provides some examples of the sorts of information that can be stored in the Pedagogical model, the Domain model and the Learner model.

AIEd Models.jpg

To delve deeper into just one of these examples, learner models are ways of representing the interactions that happen between the computer and the learner. The interactions represented in the model (such as the student’s current activities, previous achievements, emotional state, and whether or not they followed feedback) can then be used by the domain and pedagogy components of an AIEd programme to infer the success of the learner (and teacher). The domain and pedagogy models also use this information to determine the next most appropriate interaction (learning materials or learning activities). Importantly, the learner’s activities are continually fed back into the learner model, making the model richer and more complete, and the system ‘smarter’.


This post is an adapted extract from Intelligence Unleashed published by Pearson.

Artificial Intelligence in Education provides smart knowledge modeling tools.

At the heart of AI in Education (AIEd) is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.”  In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper, and more fine-grained understandings of how learning actually happens (for example, how it is influenced by the learner’s socio-economic and physical context, or by technology). These understandings may then be applied to the development of future AIEd software and, importantly, can also inform approaches to learning that do not involve technology.


For example, AIEd can help us see and understand the micro-steps that learners go through in learning, or the common misconceptions that arise. These understandings can then be used to good effect by classroom teachers. AI involves computer software that has been programmed to interact with the world in ways normally requiring human intelligence. This means that AI depends both on knowledge about the world, and algorithms to intelligently process that knowledge.


This knowledge about the world is represented in so called ‘models’. There are three key models at the heart of any AIEd application: the pedagogical model, the domain model, and the learner model. Take the example of an AIEd system that is designed to provide appropriate individualised feedback to a student. Achieving this requires that the AIEd system knows something about:
• Effective approaches to teaching (which is represented in a pedagogical model);
• The subject being learned (represented in the domain model);
• The student (represented in the learner model);

In my next post I’ll provide examples of the sorts of information that can be found in each of these models.

This post is an adapted extract from Intelligence Unleashed published by Pearson.